KIPS Transactions on Software and Data Engineering (정보처리학회논문지:소프트웨어 및 데이터공학)

Korea Information Processing Society (한국정보처리학회)
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A Classification Method of Delirium Patients Using Local Covering-Based Rule Acquisition Approach with Rough Lower Approximation

러프 하한 근사를 갖는 로컬 커버링 기반 규칙 획득 기법을 이용한 섬망 환자의 분류 방법

  • 손창식 (대구경북과학기술원 지능형로봇연구부) ;
  • 강원석 (대구경북과학기술원 지능형로봇연구부) ;
  • 이종하 (계명대학교 의과대학 의용공학과) ;
  • 문경자 (계명대학교 간호학과)
  • Received : 2019.12.20
  • Accepted : 2020.01.29
  • Published : 2020.04.30
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Abstract

Delirium is among the most common mental disorders encountered in patients with a temporary cognitive impairment such as consciousness disorder, attention disorder, and poor speech, particularly among those who are older. Delirium is distressing for patients and families, can interfere with the management of symptoms such as pain, and is associated with increased elderly mortality. The purpose of this paper is to generate useful clinical knowledge that can be used to distinguish the outcomes of patients with delirium in long-term care facilities. For this purpose, we extracted the clinical classification knowledge associated with delirium using a local covering rule acquisition approach with the rough lower approximation region. The clinical applicability of the proposed method was verified using data collected from a prospective cohort study. From the results of this study, we found six useful clinical pieces of evidence that the duration of delirium could more than 12 days. Also, we confirmed eight factors such as BMI, Charlson Comorbidity Index, hospitalization path, nutrition deficiency, infection, sleep disturbance, bed scores, and diaper use are important in distinguishing the outcomes of delirium patients. The classification performance of the proposed method was verified by comparison with three benchmarking models, ANN, SVM with RBF kernel, and Random Forest, using a statistical five-fold cross-validation method. The proposed method showed an improved average performance of 0.6% and 2.7% in both accuracy and AUC criteria when compared with the SVM model with the highest classification performance of the three models respectively.

섬망은 의식 장애, 주의력 장애 및 언어력 장애와 같은 일시적인 인지 장애가 있는 환자, 특히 노인에서 나타나는 가장 흔한 정신 장애 중 하나이다. 섬망은 환자와 가족에게 고통을 주고, 통증과 같은 증상의 관리를 방해할 수 있으며 노인 사망률 증가와 관련이 있다. 본 논문의 목적은 장기 요양 시설에서 섬망 환자를 구별하는데 사용될 수 있는 유용한 임상적 지식을 생성하는데 있다. 이러한 목적을 위해, 러프 하한 근사 영역을 갖는 로컬 커버링 규칙 기법을 활용하여 섬망과 관련된 임상적 분류 지식을 추출하였다. 제안된 방법의 임상적 적용 가능성은 전향적 코호트 연구로부터 수집된 데이터를 활용하여 확인하였다. 연구 결과, 섬망 기간이 12일 이상 지속될 수 있는 6가지 유용한 임상적 증거를 발견하였고, 체질량 지수, 동반질환 지수, 입원경로, 영양결핍, 감염, 수면박탈, 욕창, 기저귀 사용과 같은 8가지 인자들이 섬망 결과를 구별하는 데 중요한 요인이라는 것을 확인하였다. 제안된 방법의 분류 성능은 통계적 5-겹 교차검정 방법을 사용하여 3가지 벤치마킹 모델, 즉 ANN, RBF 커널 함수를 활용한 SVM, 랜덤 포레스트와 비교하여 검증하였다. 제안된 방법은 3가지 모델 중 가장 높은 성능을 제공한 SVM 모델과 비교했을 때 정확도와 AUC 기준에서 평균 0.6%와 2.7% 개선된 성능을 보였다.

Keywords

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